Forecasting plant and crop disease: an explorative study on current algorithms

G Fenu, FM Malloci - Big Data and Cognitive Computing, 2021 - mdpi.com
Every year, plant diseases cause a significant loss of valuable food crops around the world.
The plant and crop disease management practice implemented in order to mitigate …

A smartphone-based application for scale pest detection using multiple-object detection methods

JW Chen, WJ Lin, HJ Cheng, CL Hung, CY Lin… - Electronics, 2021 - mdpi.com
Taiwan's economy mainly relies on the export of agricultural products. If even the suspicion
of a pest is found in the crop products after they are exported, not only are the agricultural …

Machine learning techniques for coffee classification: a comprehensive review of scientific research

IVC Motta, N Vuillerme, HH Pham… - Artificial Intelligence …, 2024 - Springer
In the realm of agribusiness, transformative shifts are underway, propelled by the growing
demands and expanding scales of grain production. This evolution calls for a critical …

Monitoring wheat fusarium head blight using unmanned aerial vehicle hyperspectral imagery

L Liu, Y Dong, W Huang, X Du, H Ma - Remote Sensing, 2020 - mdpi.com
The monitoring of winter wheat Fusarium head blight via rapid and non-destructive
measures is important for agricultural production and disease control. Images of unmanned …

IoT-enabled IEEE 802.15. 4 WSN monitoring infrastructure-driven fuzzy-logic-based crop pest prediction

RP Sharma, D Ramesh, P Pal… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Precision agriculture, as the future of farming technology, addresses challenges faced by
farmers by data mining of information collected through IoT-enabled crop monitoring …

Development and validation of innovative machine learning models for predicting date palm mite infestation on fruits

M Mohammed, H El-Shafie, M Munir - Agronomy, 2023 - mdpi.com
The date palm mite (DPM), Oligonychus afrasiaticus (McGregor), is a key pest of unripe date
fruits. The detection of this mite depends largely on the visual observations of the webs it …

Characteristic features of statistical models and machine learning methods derived from pest and disease monitoring datasets

S Kishi, J Sun, A Kawaguchi, S Ochi… - Royal Society …, 2023 - royalsocietypublishing.org
While many studies have used traditional statistical methods when analysing monitoring
data to predict future population dynamics of crop pests and diseases, increasing studies …

RustOnt: an ontology to explain weather favorable conditions of the coffee rust

C Suarez, D Griol, C Figueroa, JC Corrales… - Sensors, 2022 - mdpi.com
Crop disease management in smart agriculture involves applying and using new
technologies to reduce the impact of diseases on the quality of products. Coffee rust is a …

An automated pest identification and classification in crops using artificial intelligence—a state-of-art-review

J Mekha, V Parthasarathy - Automatic Control and Computer Sciences, 2022 - Springer
Agriculture in India remains the nation's livelihood and a deciding factor for the Indian
economy. Among the challenges the planters face from seeding to harvesting, the infestation …

Prediction of Visual Acuity after anti‐VEGF Therapy in Diabetic Macular Edema by Machine Learning

Y Zhang, F Xu, Z Lin, J Wang, C Huang… - Journal of Diabetes …, 2022 - Wiley Online Library
Purpose. To predict visual acuity (VA) 1 month after anti‐vascular endothelial growth factor
(VEGF) therapy in patients with diabetic macular edema (DME) by using machine learning …